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\section{Introduction}
In this report, we explore techniques to apply collaborative filtering to
predicting user's star ratings of movies based on previous ratings entered.
Improving the accuracy of these models is important since it helps recommender
systems to better identify content users would like and could be applied to
effectively serving advertisements and hopefully improve revenue. Nevertheless,
collaborative filtering introduces new problems to constructing models since
the algorithms to build the models must cope with missing fields in the
datasets. We present one of our algorithms for collaborative filtering on the
MovieLens 100k Dataset \cite{movielens} of $100,000$ ratings of \numusers{} users of
\nummovies{} movies. 

The remainder of this report is organized as follows. 
Section \ref{sec:dataset} gives background on the dataset. 
Section \ref{sec:algorithm} explains the algorithm used to build our model.
Section \ref{sec:parameters} describes how we tuned our parameters to improve
predictive power.
Section \ref{sec:results} presents the results and evaluation of our model.
Section \ref{sec:conclusion} concludes. 
